EAN: 9783319219028

UNDERGRADUATE TOPICS IN COMPUTER SCIENCE INTR...

This gentle introduction to High Performance Computing (HPC) for Data Science using the Message
Passing Interface (MPI) standard has been designed as a first course for undergraduates on
parallel programming on distributed memory models  and requires only basic programming notions.
Divided into two parts the first part covers high performance computing using C++ with the
Message Passing Interface (MPI) standard followed by a second part providing high-performance
data analytics on computer clusters. In the first part  the fundamental notions of blocking
versus non-blocking point-to-point communications  global communications (like broadcast or
scatter) and collaborative computations (reduce)  with Amdalh and Gustafson speed-up laws are
described before addressing parallel sorting and parallel linear algebra on computer clusters.
The common ring  torus and hypercube topologies of clusters are then explained and global
communication procedures on these topologies are studied. This first part closes with the
MapReduce (MR) model of computation well-suited to processing big data using the MPI framework.
In the second part  the book focuses on high-performance data analytics. Flat and hierarchical
clustering algorithms are introduced for data exploration along with how to program these
algorithms on computer clusters  followed by machine learning classification  and an
introduction to graph analytics. This part closes with a concise introduction to data core-sets
that let big data problems be amenable to tiny data problems. Exercises are included at the end
of each chapter in order for students to practice the concepts learned  and a final section
contains an overall exam which allows them to evaluate how well they have assimilated the
material covered in the book.

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